Technical report Modelling cancer survival at the regional and national level in Italy

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1 Technical report Modelling cancer survival at the regional and national level in Italy Enrico Grande, Riccardo Inghelmann Reparto di Epidemiologia dei Tumori, Centro Nazionale di Epidemiologia, Sorveglianza e Promozione della Salute Istituto Superiore di Sanità, Roma 1. Introduction The program named I tumori in Italia, has been established by the Istituto Superiore di Sanità and the Fondazione IRCCS Istituto Nazionale dei Tumori, in collaboration with the Italian Cancer Registries Association (AIRTum), with the objective to provide up-to-date and systematic estimates of the main epidemiological indicators at the national and regional level in Italy, for the most important cancers. The estimates were derived by using the MIAMOD method, that requires to be applied cancer-specific relative survival at the regional and national scale. Survival included in the MIAMOD model should be either tabulated or model-based. Model-based rather than tabulated survival is able to incorporate data from registries covering different time periods, and provides smoother estimates of survival age and time trends. Moreover, it allows extrapolation of estimated survival time trends. Details of the model-based survival are reported in detail in this technical report for all the cancer sites included in the program. 2. Materials Model-based survival was obtained by using relative survival data of the EUROCARE-3 study 1 cancer registries and concern cancer patients diagnosed during the period and followed up to Multiple tumours as well as deaths certificate only cases (DCO) and cases discovered at autopsy were excluded from the survival analysis. The EUROCARE-3 study included the following 13 cancer registries: Turin, Varese, Genoa (North-West of Italy), Veneto, Modena, Ferrara, Parma, Romagna (North-East), Tuscany, Macerata, Latina (Centre), Ragusa and Sassari (South), jointly covering about 16% of the national population. Table 1 shows the list of the contributing cancer registries, the corresponding percentage of coverage with respect to the regional population and the incidence period of the cases in the cancer registries areas. Relative survival (RS) figures specific by sex, period of diagnosis (5 intervals derived by the aggregation of single years: , , , , ), age (5 age classes:,,,, ) and registry were based on the Hakulinen method Methods Regional and national survival estimates, used in the MIAMOD models, has been obtained by applying parametric cure models 3 to the relative survival figures observed in the Italian cancer registries. Cure models assume that there is a proportion of cured patients P, experiencing over time a mortality risk equal to that of the general population with the same sex and age (relative survival equal to one). The relative survival for the remaining proportion of "fatal" cases (1-P) is assumed to be Weibull distributed. The relative survival function of the whole group of patients is then obtained as a mixture of the corresponding survivals for the two subgroups (fatal and cured cases). Two categorical covariates (geographical area and age) and one continuous covariate (period of diagnosis) have been included in the model, that was estimated for each sex separately. Given the incomplete coverage of cancer registration in Italian regions, two separate survival models were identified and used in the estimation procedures. In the first model (regional model) the geographical area covariate is was defined at the regional level, and the corresponding survival estimates were used for regions with locally available survival data. The second model (macro-area 1

2 model) considered the geographical area covariate at a wider level (macro-area * ). The corresponding survival estimates were derived by using data of the cancer registries included in the macro-area and were then extended to all the regions belonging to the macro-area, regardless if they have or not cancer registries inside. The two models were similar with respect to their analytical expression, let us consider the case of the macro-area model. The relative survival S x,j (d,t) at follow up time d and period of diagnosis t, for the j th region (within the macro-area) and for the age class x, is given by: with exp[ ßj(t-t)+ RRj] S x, j ( d, t) = [ S x ( d)], (1) γ x S ( d) = P + (1- P )exp[-(( λ d) )]. x x x where S x (d) represents the baseline modelled survival in a macro-area. The constant t is set equal to 1986, the central value of the calendar period Model parameters are: P x (proportion of cured cases), λ x and γ x, (scale and shape Weibull s parameters) specific for each ageclass x, β j (log relative risk of dying) and RR j (log relative excess death risk for the j th area) common to all ages. The mean survival time of fatal cases for the age class x, T x, is given by λ 1 Γ(1 + γ 1 ), where Γ is the gamma function. According to time trends observed in site-specific survival by cancer registry, whether homogeneous or variable among regions, the time trend parameter β may be: specific by macroarea (or region) or unique and common to all macro-areas (or regions). All cancers combined supported the modelling of relative survival specific for those regions sufficiently covered by cancer registration. The national survival has been obtained as the weighted average of the macro-areas estimates, using as weights the proportions of expected incident cases 4,5 in each macro-area. The methodology used to derive regional and national estimates of relative survival for specific cancer site is presented in specific papers 6,7. x (2) Table 1. List of the Italian Cancer Registries (CRs) by region with the percentage of regional coverage and the reference period. Geographical area and regions Regional population 1 Cancer Registry (CR) CR covered area % of regional population covered by CRs Reference period North-West Piemonte 4,259,212 Torino Town of Torino Lombardia 8,778,383 Varese Province of Varese Liguria 1,658,332 Genova Town of Genova North-East Veneto 4,319,611 Veneto Eleven Health Districts in the Veneto Region Emilia Romagna 3,873,062 Ferrara Province of Ferrara Modena Province of Modena Parma Province of Parma Romagna Provinces of Forlì, Ravenna and Rimini Centre Toscana 3,475,528 Toscana Provinces of Firenze and Prato Marche 1,407,777 Macerata Province of Macerata Lazio 5,058,317 Latina Province of Latina South Sicilia 4,909,441 Ragusa Province of Ragusa Sardegna 1,630,193 Sassari Province of Sassari Average total population of the region in the referece period (age 0-84 years). Emilia Romagna average population refers to the period * According to the standard classification of the Italian territory, regions have been aggregated into 4 geographical macro-areas as follows: North-West (Liguria, Lombardia, Piemonte, Valle D Aosta), North-East (Emilia Romagna, Friuli Venezia Giulia, Trentino Alto Adige, Veneto), Centre (Lazio, Marche, Toscana, Umbria), South (Abruzzo, Basilicata, Calabria, Campania, Molise, Puglia, Sardegna, Sicilia). 2

3 4. Results This section reports the complete set of survival model parameters estimated for the specific cancer sites. The national estimate of relative survival by age class and period of diagnosis is also reported for each cancer site. 3

4 4.1 Lung cancer Survival levels for lung cancer were rather low in the study period and their rate of increase was quite uniform across the country, therefore we consider a unique survival period trend coefficient. This assumption corresponds to substitute β j = β in equation (1) where the index j identifies the geographical area. A specific log relative excess death risk for the j th area RR j is assumed either at regional or at macro-area level. The Latina cancer registry, located on the border between Centre and South, has been considered for the estimation of survival in both these areas. For the region of Lazio, Centre and not Latina relative survival has been used, in order to line up this region with the rest of the area. LUNG CANCER. Estimates of regional and macro-area models parameters, by age class and sex. Regional Macro-area Age class Estimate Std Error Estimate Std Error Estimate Std Error Estimate Std Error RR (Area Relative Excess Risk) Period relative risk β (Regions) Piemonte Lombardia Liguria Veneto Emilia Romagna* Toscana Marche Sardegna Sicilia (Macro-areas) North West North East* Centre South Italy *Reference region/macro-area 4

5 LUNG CANCER. National estimates of relative survival at 5 years since diagnosis, by sex, age class and period of diagnosis (%) and annual percent change. Standardized values (Eurocare population) are reported for the age class Age Period Annual percent change

6 4.2 Stomach cancer In the relative survival estimation procedure, we considered a unique survival period trend coefficient. This assumption corresponds to substitute β j = β in equation (1) where the index i identifies the geographical area. A specific log relative excess death risk for the j th area RR j was assumed either at regional or at macro-area level. The Latina cancer registry, located on the border between Centre and South, has been considered for the estimation of survival in both these areas. STOMACH CANCER. Estimates of regional and macro-area models parameters, by age class and sex. Regional Macro-area Age class Estimate Std Error Estimate Std Error Estimate Std Error Estimate Std Error RR (Area Relative Excess Risk) Period relative risk β (Regions) Piemonte Lombardia Liguria Veneto Emilia Romagna Toscana Marche Lazio Sardegna Sicilia* (Macro-areas) North West North East* Centre South Italy *Reference region/area 6

7 STOMACH CANCER. National estimates of relative survival at 5 years since diagnosis, by sex, age class and period of diagnosis (%) and annual percent change.standardized values (Eurocare population) are reported for the age class Age Periods Annual percent change

8 4.3 Prostate cancer Survival levels for prostate cancer were rather different in the study period and their rate of increase differed across the Country, therefore we considered a different survival period trend coefficient. This assumption corresponds to use a β j parameter in equation (1) different for each geographical area j. A specific log relative excess death risk for the j th area RR j is assumed either at regional and macro-area level. The Latina cancer registry, located on the border between Centre and South, has been considered for the estimation of survival in both these areas. For the regions of Piemonte-Valle d Aosta, Liguria, Veneto, Marche, Lazio and Sardegna, the relative survival estimated for the corresponding macro-area was used. PROSTATE CANCER. Estimates of regional and macro-area models parameters, by age class. Age class Regional Macro-area Estimate Std Error Estimate Std Error (Regions) Piemonte Lombardia Liguria Veneto Emilia Romagna Toscana Lazio Period relative risk β Sicilia RR (Area Relative Excess Risk) (Macro-areas) North West North East Centre South Italy (Regions) Piemonte Lombardia Liguria Veneto Emilia Romagna* Toscana Lazio Sicilia (Macro-areas) North West North East* Centre South Italy *Reference region/macro-area 8

9 PROSTATE CANCER. National estimates of relative survival at 5 years since diagnosis, by age class and period of diagnosis (%) and annual percent change. Standardized values (Eurocare population) are reported for the age class Age Periods Annual percent change ()

10 4.4 Breast cancer In order to obtain stable estimates of relative survival in all the Italian regions, the geographical area covariate entered the model only at the macro-area level. Specific period trend coefficient (β j ) and log relative excess death risk (RR j ) for the j th macro-area were considered in the survival modelling. The Centre-South macro-area was used for the insular region Sardegna, which has a survival level intermediate among those of central and southern regions. FEMALE BREAST CANCER. Estimates of the model parameters (macro-area), by age class. Age class Estimate Std Error Period relative risk β RR (Area Relative Excess Risk) (Macro-areas) North West North East Centre South Centre-South Italy (Macro-areas) North West North East Centre South* Centre-South Italy *Reference region/macro-area 10

11 FEMALE BREAST CANCER. National estimates of relative survival (%) at 5 years since diagnosis, by age class and period of diagnosis, and annual percent change. Standardized values (Eurocare population) are reported for the age class Age Periods Annual percent change ()

12 4.5 Colorectal cancer Specific period trend coefficients (β j ) and log relative excess death risks (RR j ) for each j th region/macro-area were considered in the survival modelling, due to the different survival timetrends and levels observed among the Italian CRs. More stable macro-area estimates, instead of regional estimates, were used also for some regions covered by cancer registration, like Lombardia, Veneto, Marche and Sardegna COLORECTAL CANCER. Estimates of regional and macro-area models parameters, by age class and sex. Age class Period relative risk β RR (Area Relative Excess Risk) Regional Macro-area Estimate Std Error Estimate Std Error Estimate Std Error Estimate Std Error (Regions) Piemonte Liguria Lombardia Emilia Romagna Toscana Lazio Sicilia (Macro-areas) North West North East Centre South Italy (Regions) Piemonte Liguria Lombardia* Emilia Romagna Toscana Lazio Sicilia (Macro-areas) North West* North East Centre South Italy *Reference region/macro-area 12

13 COLORECTAL CANCER. National estimates of relative survival (%) at 5 years since diagnosis, by age class, sex and period of diagnosis, and annual percent change. Standardized values (Eurocare population) are reported for the age class Age Periods Annual percent change

14 4.6 All cancer malignancies The model used to derive regional survival for all cancer sites combined did not include any geographical area covariate, but it was separately estimated for each sex and geographical area. It represents a basic form of the model given in the equation (1), in which the time trend parameter was not depending on the geographical area and no relative excess death risk was estimated: S x ( d, t) = [ S x ( d)] exp[ ß(t-t)] where S x (d) represents the modelled survival in a geographical area, as in equation (2). The geographical areas considered for the survival estimation were: - regions, when long-term local CR s data were available, i.e. Lombardia (Varese), Emilia- Romagna (Parma), and Sicily (Ragusa); - macro-areas (North-Centre and South), to obtain stable survival estimates in the regions with more recently established CRs or without any cancer registration system at all. The northern-central area included the registries Turin, Varese, Genoa, Veneto, Modena, Ferrara, Parma, Romagna, Tuscany, Macerata and Latina. The survival estimate obtained for this area was attributed to the regions Piemonte, Valle d Aosta, Liguria, Trentino Alto Adige, Veneto, Friuli Venezia Giulia (Northern Italy), Toscana, Umbria, Marche, and Lazio (Central Italy). The southern area included data from the registries of Ragusa, Sassari and Latina, and was used to derive survival estimates for the regions Abruzzo, Molise, Campania, Puglia, Basilicata, Calabria, and Sardegna. The national estimate was obtained from the modelling approach applied in a recently published work 6. ALL CANCER MALIGNANCIES. Estimates of the model parameters by age class and sex. Geographical area: Lombardia Age Class Estimate Std Error Estimate Std Error Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Period relative risk β

15 ALL CANCER MALIGNANCIES. Estimates of the model parameters by age class and sex. Geographical area: Emilia Romagna Age Class Estimate Std Error Estimate Std Error Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Period relative risk β ALL CANCER MALIGNANCIES. Estimates of the model parameters by age class and sex. Geographical area: Sicilia Age Class Estimate Std Error Estimate Std Error Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Period relative risk β

16 ALL CANCER MALIGNANCIES. Estimates of the model parameters by age class and sex. Geographical area: North-Centre Age Class Estimate Std Error Estimate Std Error Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Period relative risk β ALL CANCER MALIGNANCIES. Estimates of the model parameters by age class and sex. Geographical area: South Age Class Estimate Std Error Estimate Std Error Weibull's parameters λ Weibull's parameters γ * -* Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Period relative risk β *Estimates of Mean Survival Time for this age group were not considered reliable 16

17 ALL CANCER MALIGNANCIES. Estimates of the model parameters by age class and sex. Geographical area: Italia Age Class Estimate Std Error Estimate Std Error Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Weibull's parameters λ Weibull's parameters γ Period relative risk β All Cancers. National estimates of relative survival at 5 years since diagnosis, by sex, age class and period of diagnosis (%) and age-specific annual mean growth rate (%). Standardized values (Eurocare population) are reported for the age class Age Periods Annual percent change

18 References 1. Roazzi P, Capocaccia R, Santaquilani M, Carrani E and the EUROCARE Working Group. Electronic availability of EUROCARE-3 data: a tool for further analysis. Ann Oncol 2003, 14 (Suppl. 5), Hakulinen T: Cancer survival corrected for heterogeneity in patient withdrawal. Biometrics, 38: , De Angelis R, Capocaccia R, Hakulinen T, Soderman B and Verdecchia A: Mixture models for cancer survival analysis: application to population-based data with covariates. Statistics in Medicine, 18: , Zanetti R, Gafà L, Pannelli F, et al, editors. Cancer in Italy, incidence data from cancer registries, Roma, Il Pensiero Scientifico Editore, Rosso S, Spitale A, Balzi D, Franceschi S, Zanetti R: Stima dell incidenza dei tumori nelle regioni italiane nel Epidemiologia e Prevenzione 28 (4-5): , Inghelmann R, Grande E, Francisci S, De Angelis R, Micheli A, Verdecchia A, Ferretti S, Vercelli M, Ramazzotti V, Pannelli F, Federico M, De Lisi V, Tumino R, Falcini F, Budroni M, Zanetti R, Paci E, Crosignani P, Zambon P, and Capocaccia R: National estimates of cancer patients survival in Italy: a model-based method. Tumori, 91: , Verdecchia A., De Angelis R., Francisci S., Grande E: Methodology for estimation of cancer incidence, survival and prevalence in Italian regions. Tumori, 93, ,